Nearly two decades have passed since Automatic External Defibrillators (AEDs) were created to help reduce incidents of cardiac arrest. Over that time, AEDs have become more prevalent in public locales such as offices, shopping centers stadiums and other areas of high pedestrian traffic. The AEDs empower citizens to provide medical help during cardiac emergencies in public places where help was previously unavailable in the crucial early stages of a cardiac event. In recent years, fully automated external defibrillators capable of accurately detecting ventricular arrhythmia and non-shockable supraventricular arrhythmia, such as those described in U.S. Pat. No. 5,474,574 to Payne et al., were developed to treat unattended patients. These devices treat victims suffering from ventricular arrhythmias and have high sensitivity and specificity in detecting shockable arrhythmias in real-time. Further, AEDs have been developed to serve as diagnostic monitoring devices that can automatically provide therapy in hospital settings as exhibited in U.S. Pat. No. 6,658,290 to Lin et al.
In addition to advances in the field of AEDs, there have been several advancements in the understanding of human physiology and how it relates to medical care. These advancements in medical research have lead to the development of new protocols and standard operating procedures in dealing with incidents of physical trauma. For example, in public access protocols for defibrillation, recent guidelines have emphasized the need for cardio-pulmonary resuscitation (CPR) along with use of AEDs. In fact, recent American Heart Association (AHA) Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care suggest that AEDs may be further integrated into emergency response protocols by detecting shockable rhythms, applying a shock and then prompting the rescuer to resume compressions immediately. (American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care, IV-36, American Heart Association, Inc., 2005). Further, the guidelines comment that AEDs may be developed that further retrain or assist the rescuer in direction, specifically reducing the number of withheld compressions due to reassessment of the patient and ensuring efficient transfer to trained medical professionals. The guidelines, along with independent research, led to an inclusive approach involving defibrillation, along with CPR, as the suggested method for AED device use.
Current AEDs, while providing defibrillation, are not functional in implementing the current suggested methods of AED use as recommended by the guidelines. Most of the AEDs available today attempt to classify ventricular rhythms. Specifically, current AEDs attempt to distinguish between shockable ventricular rhythms and all other rhythms that are non-shockable. This detection and analysis of ventricular rhythms requires real-time analysis of ECG waveforms. Thus, the functionality, accuracy and speed of the AED heavily depend on the algorithms and hardware utilized for real time analysis of ECG waveforms.
In many implementations, the algorithms depend on heart rate calculations and a variety of morphology features derived from ECG waveforms, like ECG waveform factor and irregularity as disclosed in U.S. Pat. No. 5,474,574 to Payne et al. and U.S. Pat. No. 6,480,734 to Zhang et al. Further, in order to provide sufficient processing capability, current AEDs commonly embed the algorithms and control logic into microcontollers.
It has been noted, that current algorithmic and specific hardware implementations can have a profound impact on the effectiveness of the AED. Specifically, the signal-to-noise ratio of ECG signals greatly effects AED performance. For example, during a rescue operation, algorithms implemented in many current AEDs require a few seconds of clean ECG signal data to classify a sensed ventricular rhythm. During cardiopulmonary resuscitation where a rescuer may apply chest compressions and relaxations, at a prescribed rate, close to 100 cycles per minute, the chances of obtaining such clean signal data are significantly reduced. In practice, the chest compressions and relaxations introducing significant motion artifacts in an ECG recording. In addition, ECG signals exhibit poor amplitudes during ventricular arrhythmia events, further reducing signal-to-noise ratios, often resulting in low quality or unusable signals. In these conditions, existing arrhythmia recognition algorithms may not perform adequately, leaving afflicted persons at risk.
Attempts have been made to reduce the effect of sensory artifacts by altering the designs of ECG electrodes and the analog front-end circuitry. One design implements a lower cut-off frequency for the high pass cut-off in ECG amplifiers. Other designs utilize differential amplifiers with very high common mode rejection ratio (CMRR) to attempt to avoid artifacts to an extent. However, in these designs it is essential to capture a good quality signal in the digital domain in order to remove any artifacts using digital logic and algorithms. This is mainly due to the fact that signal quantity lost as a result of saturation effects during analog to digital conversion is not recoverable using current known techniques.
In addition to the designs of electrodes, the current algorithms are not effective in artifact filtering under current standards and practices for CPR. One of the present challenges is to identify a shockable cardiac rhythm even during CPR compression cycles and to identify non-shockable/recovery rhythms in real-time. Because asystole condition is an important metric another challenge is to accurately detect asystole. Various methods for the identification and removal of CPR artifacts that can corrupt an ECG signal have been proposed. For instance, U.S. Pat. No. 6,961,612 utilizes a reference signal in attempting to remove artifacts. U.S. Pat. No. 7,039,457 provides an algorithm that relies on assumptions as to the operation of the cardiac system, along with a reference signal. U.S. Pat. No. 6,807,442, uses multiple sensors as indicators of CPR activity and to provide reference signals. U.S. Pat. No. 6,961,612 utilizes a reference signal indicative of CPR activity to identify the presence of CPR artifacts in an ECG segment. WO/2006/015348 discloses utilizing a transthoracic impedance measurement to identify significant patient motion. U.S. Pat. No. 5,704,365 describes utilizing a plurality of ECG leads to estimate the effect of noise on ECG signals. U.S. Pat. No. 7,295,871 discloses a frequency domain approach to system identification using linear predictive filtering and recursive least squares. In some recent research, K. Rhineberger introduced an alternative method of ECG filtering based on adaptive regression on lagged reference signals (Rheinberger, et al., Removal of resuscitation artifacts from ventricular fibrillation ECG signals using kalman methods, Computers in Cardiology (2005)). Still other methods of CPR artifact detection and filtering focus on utilizing frequency modulation instead of a reference signal to remove anomalies. (See Aramendi et al., Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artifacts, Resuscitation (2007)). Other disclosed methods of implementing care in response situations focus on the detection and determination of CPR activity and utilize chest compression detectors (EP 1859770 A1) or accelerometers (U.S. Pat. No. 7,122,014) to estimate the depth and presence of CPR compressions.
However, all of these platforms or methods have limitations and concerns when providing real time care under recent American Heart Association CPR guidelines. Thus, a method and apparatus for filtering CPR artifacts from ECG signals that is effective over the diverse range of ECG segments, is computationally inexpensive and exhibits near real-time analysis and filtering thus enabling a clean ECG signal for determining shockable and non-shockable states is desired.